Selecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization
This addresses utility investment planning challenges by efficiently identifying high-risk DER adoption scenarios to prevent voltage and line flow violations.
The paper tackles the problem of selecting critical scenarios of distributed energy resource (DER) adoption for distribution grid planning by developing a multi-objective Bayesian optimization framework that provides statistical guarantees and achieves an order of magnitude speed-up compared to exhaustive search methods.
We develop a new methodology to select scenarios of DER adoption most critical for distribution grids. Anticipating risks of future voltage and line flow violations due to additional PV adopters is central for utility investment planning but continues to rely on deterministic or ad hoc scenario selection. We propose a highly efficient search framework based on multi-objective Bayesian Optimization. We treat underlying grid stress metrics as computationally expensive black-box functions, approximated via Gaussian Process surrogates and design an acquisition function based on probability of scenarios being Pareto-critical across a collection of line- and bus-based violation objectives. Our approach provides a statistical guarantee and offers an order of magnitude speed-up relative to a conservative exhaustive search. Case studies on realistic feeders with 200-400 buses demonstrate the effectiveness and accuracy of our approach.